Edge enhancement filter

ABSTRACT

A method, computer program, and computer system is provided for coding video data. Video data is received, and an edge present within a sample of the received video data is detected. A gradient value corresponding to a direction associated with the detected edge is calculated. The video data is decoded based on the calculated gradient.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. application Ser. No.17/077,488, filed Oct. 22, 2020, which claims priority from U.S.Provisional Patent Application No. 63/011,895, filed on Apr. 17, 2020,in the U.S. Patent and Trademark Office, the contents of all of whichare incorporated herein by reference in their entireties.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video encoding and decoding.

BACKGROUND

AOMedia Video 1 (AV1) is an open video coding format designed for videotransmissions over the Internet. It was developed as a successor to VP9by the Alliance for Open Media (AOMedia), a consortium founded in 2015that includes semiconductor firms, video on demand providers, videocontent producers, software development companies and web browservendors. Many of the components of the AV1 project were sourced fromprevious research efforts by Alliance members. Individual contributorsstarted experimental technology platforms years before: Xiph's/Mozilla'sDaala already published code in 2010, Google's experimental VP9evolution project VP10 was announced on 12 Sep. 2014, and Cisco's Thorwas published on 11 Aug. 2015. Building on the codebase of VP9, AV1incorporates additional techniques, several of which were developed inthese experimental formats. The first version 0.1.0 of the AV1 referencecodec was published on 7 Apr. 2016. The Alliance announced the releaseof the AV1 bitstream specification on 28 Mar. 2018, along with areference, software-based encoder and decoder. On 25 Jun. 2018, avalidated version 1.0.0 of the specification was released. On 8 Jan.2019 a validated version 1.0.0 with Errata 1 of the specification wasreleased. The AV1 bitstream specification includes a reference videocodec.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo coding. According to one aspect, a method for video coding isprovided. The method may include receiving and/or transmitting videodata. An edge present within a sample of the video data is detected. Agradient value corresponding to a direction associated with the detectededge is calculated. The video data is decoded and/or encoded based onthe calculated gradient.

According to another aspect, a computer system for video coding isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving and/or transmitting video data. An edge present withina sample of the video data is detected. A gradient value correspondingto a direction associated with the detected edge is calculated. Thevideo data is decoded and/or encoded based on the calculated gradient.

According to yet another aspect, a computer readable medium for videocoding is provided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving and/or transmitting video data. An edge present withina sample of the video data is detected. A gradient value correspondingto a direction associated with the detected edge is calculated. Thevideo data is decoded and/or encoded based on the calculated gradient.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a diagram of exemplary edge directions, according to at leastone embodiment;

FIG. 3 is a diagram of exemplary gradient calculations, according to atleast one embodiment;

FIG. 4 is an operational flowchart illustrating the steps carried out bya program that codes video data based on detecting an edge presentwithin the video data, according to at least one embodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1 , according to at leastone embodiment; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6 , according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to video encoding and decoding. The following describedexemplary embodiments provide a system, method and computer program to,among other things, encode and decode video data based on calculatinggradients of detected edges within video data. Therefore, someembodiments have the capacity to improve the field of computing by forimproved encoding and decoding of video data through improved detectingof edges in the video data.

As previously described, AOMedia Video 1 (AV1) is an open video codingformat designed for video transmissions over the Internet. It wasdeveloped as a successor to VP9 by the Alliance for Open Media(AOMedia), a consortium founded in 2015 that includes semiconductorfirms, video on demand providers, video content producers, softwaredevelopment companies and web browser vendors. Many of the components ofthe AV1 project were sourced from previous research efforts by Alliancemembers. Individual contributors started experimental technologyplatforms years before: Xiph's/Mozilla's Daala already published code in2010, Google's experimental VP9 evolution project VP10 was announced on12 Sep. 2014, and Cisco's Thor was published on 11 Aug. 2015. Buildingon the codebase of VP9, AV1 incorporates additional techniques, severalof which were developed in these experimental formats. The first version0.1.0 of the AV1 reference codec was published on 7 Apr. 2016. TheAlliance announced the release of the AV1 bitstream specification on 28Mar. 2018, along with a reference, software-based encoder and decoder.On 25 Jun. 2018, a validated version 1.0.0 of the specification wasreleased. On 8 Jan. 2019 a validated version 1.0.0 with Errata 1 of thespecification was released. The AV1 bitstream specification includes areference video codec.

In AV1, loop filters deal with the artifacts and information loss duringvideo compression process. The said loop filters include but not limitedto ALF, SAO, Deblocking, CDEF, CCALF, LR. CCALF is a high-pass filterwhich enhances edges in chroma components. First, like traditionalfiltering methods, CCALF needs to signal multiple sets of filtercoefficient. Moreover, CCALF can be only applied on chroma components.However, lossy coding modules introduce information loss. An example ofinformation loss is weakening or disappearing of edges aftercompression. This may affect coding efficiency and visual quality. Itmay be advantageous, therefore, to determine edge directions and edgepositions using samples that belong to a first and/or a second colorcomponent, compute a gradient value that specifies the increase ordecrease of sample values using the samples, and adding an offset value.Filter coefficients may not need to be signaled, and the filtering maybe applied on all color components, such as Y/Cb/Cr and R/G/B.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

Referring now to FIG. 1 , a functional block diagram of a networkedcomputer environment illustrating a video coding system 100 (hereinafter“system”) for encoding and decoding video data based on detecting anedge present within the video data. It should be appreciated that FIG. 1provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 5 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 6 and 7 . The server computer 114 may also be locatedin a cloud computing deployment model, such as a private cloud,community cloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for coding video data basedon detecting an edge within the video data is enabled to run a VideoCoding Program 116 (hereinafter “program”) that may interact with adatabase 112. The Video Coding Program method is explained in moredetail below with respect to FIG. 4 . In one embodiment, the computer102 may operate as an input device including a user interface while theprogram 116 may run primarily on server computer 114. In an alternativeembodiment, the program 116 may run primarily on one or more computers102 while the server computer 114 may be used for processing and storageof data used by the program 116. It should be noted that the program 116may be a standalone program or may be integrated into a larger videocoding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 maybe implemented within a single device, or a single device shown in FIG.1 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2 , a diagram of exemplary edge directions 200 isdepicted. An edge may denote a set of samples located at continuouspositions of the video and image data where an abrupt change ofintensity values occurs. An edge may have a direction that may denote aunit vector along the edge. An edge may have a position that maycorrespond to a sample position at which the edge is located. Edgedetection, therefore, may correspond to a process that may be used todetermine the edge direction and edge position. For example, edgedetection may be performed by determining a gradient along the edge. Thegradient may correspond to an increase or decrease of sample valuesalong a direction which may be different from edge direction. A negativegradient value may correspond to sample intensity changing to smallerones along a given direction, while a positive gradient value maycorrespond to sample intensity changing to larger ones along a givendirection. The edge directions 200 may include, for example, ahorizontal edge 202, a vertical edge 204, a 45-degree edge 206, and a135-degree edge 208. It may be appreciated that given a large enoughsample block size, substantially any angle of edge may exist.

Edge detection may be applied on an M×N block, where M and N arepositive integers, example values of M and N include but not limited to:1, 2, 3, 4, 8. The input of this process is an M×N block of samples, andthe output is the edge direction and edge position of the block. In oneembodiment, when M=N=1, the edge detection process is performed atpixel-level. In one embodiment. when M>1 and/or N>1, the edge detectionis performed at block-level. In one embodiment, edge direction isdetermined based on a direction search of a block having a smallesterror value. The direction search operates on the reconstructed pixels,just after the deblocking filter. Since those pixels are available tothe decoder, the directions require no signaling. The search operates on8×8 blocks, which are small enough to adequately handle non-straightedges, while being large enough to reliably estimate directions whenapplied to a quantized image. Having a constant direction over an 8×8region also makes vectorization of the filter easier. For each block adirection that best matches a pattern in the block may be determined byminimizing the sum of squared differences (SSD) between the quantizedblock and the closest perfectly directional block. A perfectlydirectional block is a block where all of the pixels along a line in onedirection have the same value. In one embodiment, edge detection isperformed using Canny Edge detector. In one embodiment, edge detectionis performed using Sobel Edge detector.

Referring now to FIG. 3 , a diagram of exemplary gradient calculations300 is depicted. The gradients may be calculated along a horizontal edge302, a vertical edge 304, a 45-degree edge 306, and a 135-degree edge308. The gradient value may be computed along a direction which mayspecify the increase or decrease of sample values. An input to thisprocess may include an M×N block, and an output of this process mayinclude a number k. In one example, when k>0, it denotes the samplevalues increase along the direction, and when k<0, it means samplevalues decrease along the direction. The gradient along a direction iscomputed as

${k = {\sum\limits_{i}\left( {y_{i} - x_{i}} \right)}},$

where x_(i) and y_(i) may be pairs of sample values along the direction.

In one embodiment, the gradient may be computed in a direction which maybe orthogonal to the edge direction. In one example, when the edge ishorizontal, vertical gradient is computed. In another example, when theedge is vertical, horizontal gradient is computed. In another example,when the edge is 45-degree, 135-degree gradient is computed. In anotherexample, when the edge is 135-degree, 45-degree gradient is computed.

An offset value may be an integer that may be added to the samples. Forexample, the offset values may include 0, +1, −1, +2, −2, +3, −3, etc.In one embodiment, offsets are added to a color component which is adifferent color component on which edge detection is applied. In oneexample, edge detection is applied on Y, offsets are added to Cb and/orCr. In one embodiment, offsets are added to a color component which isthe same color component on which edge detection is applied. In oneexample, edge detection is applied on Y, offsets are added to Y.

In one embodiment, offsets are added to all samples within the block. Inone example, when edge detection is applied on an 8×8 luma block,offsets are added to all samples within the 8×8 luma block. In oneexample, when edge detection is applied on an 8×8 luma block, offsetsare added to all samples within the corresponding 4×4 chroma block. Inone embodiment, offsets are only added to samples located on the edge,e.g., edge positions. In one example, when edge detection is applied onan 8×8 luma block, offsets are only added to samples located on the edgewithin the 8×8 luma block. In one example, when edge detection isapplied on an 8×8 luma block, offsets are only added to samples locatedon the edge within the corresponding 4×4 chroma block.

In one embodiment, clipping is performed when adding offset to ensuresample value does not exceed maximum or minimum values. In one example,clipping is performed as clip(x)=max(min(x, max_val), min_val), wheremax and min refer to maximum and minimum operator, and max_val andmin_val denote maximum and minimum sample value, x is the sample valueto be clipped.

In one embodiment, offset values are derived depending on the edgedirection and gradient. In one example, a look-up table is pre-defined,given the derived edge direction i and gradient value g as inputs to thelook-up table, the output is an offset value. This look-up table can besignaled in high-level parameter set (DPS, VPS, SPS, PPS, APS), sliceheader, picture header, SEI message or CTU-level. In one example, offsetvalues are derived depending on the edge direction and the sign ofgradient. In one example, the offset values that are applied for thesame edge direction, but different signs of gradient have different signvalues. For example, the offset value for 45-degree edge with positivegradient values and the offset value for 45-degree edge with negativegradient values have opposite signs.

Referring now to FIG. 4 , an operational flowchart illustrating thesteps of a method 400 for coding video data is depicted. In someimplementations, one or more process blocks of FIG. 4 may be performedby the computer 102 (FIG. 1 ) and the server computer 114 (FIG. 1 ). Insome implementations, one or more process blocks of FIG. 4 may beperformed by another device or a group of devices separate from orincluding the computer 102 and the server computer 114.

At 402, the method 400 includes receiving video data.

At 404, the method 400 includes detecting an edge present within asample of the received video data.

At 406, the method 400 includes calculating a gradient valuecorresponding to a direction associated with the detected edge.

At 408, the method 400 includes decoding the video data based on thecalculated gradient.

It may be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements. For example, a method without departing from the spirit ofthe described embodiments may include detecting an edge present within asample of a video data, calculating a gradient value corresponding to adirection associated with the detected edge, encoding the video databased on the calculated gradient, and transmitting the encoded videodata.

FIG. 5 is a block diagram 500 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 5 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1 ) and server computer 114 (FIG. 1 ) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4 . Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1) and the Video Coding Program 116 (FIG. 1 ) on server computer 114(FIG. 1 ) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 5 , each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1 ) and the Video Coding Program 116 (FIG. 1 ) can bestored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1 ) and theVideo Coding Program 116 (FIG. 1 ) on the server computer 114 (FIG. 1 )can be downloaded to the computer 102 (FIG. 1 ) and server computer 114from an external computer via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 836. From the network adapters or interfaces 836,the software program 108 and the Video Coding Program 116 on the servercomputer 114 are loaded into the respective hard drive 830. The networkmay comprise copper wires, optical fibers, wireless transmission,routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 6 , illustrative cloud computing environment 600 isdepicted. As shown, cloud computing environment 600 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 600 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 7 , a set of functional abstraction layers 700provided by cloud computing environment 600 (FIG. 6 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 7 are intended to be illustrative only andembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Video Coding 96. Video Coding 96 mayencode and decode video data based on detecting an edge within the videodata.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobj ect oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method for video coding, executable by aprocessor, comprising: receiving video data; detecting an edge presentwithin a sample of the received video data; calculating a gradient valuecorresponding to a direction associated with the detected edge;adjusting the sample based on the calculated gradient value and alocation of the sample within the received video data; and decoding thevideo data based on the calculated gradient value.
 2. The method ofclaim 1, wherein the edge is detected based on one or more from among aSobel edge detector, a Canny edge detector, and a direction search basedon comparing a block of the received video data to one or morepredetermined edge directions.
 3. The method of claim 1, wherein theedge detection is performed at one from among a pixel-level and ablock-level based on a size associated with the sample of the receivedvideo data.
 4. The method of claim 1, wherein the gradient valuespecifies an increase or decrease of sample values associated with thedetected edge.
 5. The method of claim 4, wherein the gradient value iscomputed in a direction orthogonal to the direction associated with thedetected edge.
 6. The method of claim 4, wherein the gradient value iscomputed by summing differences of pairs of sample values along thedetected edge.
 7. The method of claim 1, wherein the adjusting thesample includes: deriving one or more offset values based on thedirection associated with the detected edge and the calculated gradientvalue; and adding the one or more derived offset values to the sample ofthe received video data based on the location of the sample in thereceived video data.
 8. The method of claim 7, wherein the one or morederived offset values are added to a color component different from acolor component on which edge detection is applied.
 9. The method ofclaim 7, wherein the one or more derived offset values are added to acolor component that is the same as a color component on which edgedetection is applied.
 10. The method of claim 7, wherein the derived oneor more offset values are added to the sample based on a determinationthe sample is located on the detected edge.
 11. The method of claim 7,wherein a look-up table containing the one or more offset values ispre-defined based on the detected edge and calculated gradient value.12. The method of claim 7, wherein the one or more offset values arederived based on the direction of the detected edge and a sign of thegradient value.
 13. A computer system for video coding, the computersystem comprising: one or more computer-readable non-transitory storagemedia configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: receiving code configured to cause the one ormore computer processors to receive video data; detecting codeconfigured to cause the one or more computer processors to detect anedge present within a sample of the received video data; calculatingcode configured to cause the one or more computer processors tocalculate a gradient value corresponding to a direction associated withthe detected edge; adjusting code configured to cause the one or morecomputer processors to adjust the sample based on the calculatedgradient value and a location of the sample within the received videodata; and decoding code configured to cause the one or more computerprocessors to decode the video data based on the calculated gradientvalue.
 14. The computer system of claim 13, wherein the edge is detectedbased on one or more from among a Sobel edge detector, a Canny edgedetector, and a direction search based on comparing a block of thereceived video data to one or more predetermined edge directions. 15.The computer system of claim 13, wherein the edge detection is performedat one from among a pixel-level and a block-level based on a sizeassociated with the sample of the received video data.
 16. The computersystem according to claim 13, wherein the adjusting code is furtherconfigured to cause the one or more computer processors to: derive oneor more offset values based on the direction associated with thedetected edge and the calculated gradient value; and add the one or moreoffset values to the sample of the received video data based on thelocation of the sample in the received video data.
 17. The computersystem according to claim 16, wherein the derived one or more offsetvalues are added to the sample based on a determination the sample islocated on the detected edge.
 18. A non-transitory computer readablemedium having stored thereon a computer program for video coding, thecomputer program configured to cause one or more computer processors to:receive video data; detect an edge present within a sample of thereceived video data; calculate a gradient value corresponding to adirection associated with the detected edge; adjusting the sample basedon the calculated gradient value and a location of the sample within thereceived video data; and decode the video data based on the calculatedgradient value.
 19. The non-transitory computer readable mediumaccording to claim 18, wherein the adjusting the sample includes:deriving one or more offset values based on the direction associatedwith the detected edge and the calculated gradient value; and adding theone or more derived offset values to the sample of the received videodata based on the location of the sample in the received video data. 20.The non-transitory computer readable medium according to claim 19,wherein the derived one or more offset values are added to the samplebased on a determination the sample is located on the detected edge.